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Can a model trained on satellite data really find brambles on the ground?

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Re: Can a model trained on satellite data really find brambles on the ground?

#51

Post by avsm »

sadiq wrote: post #49
ensocode wrote: post #48

Earlier quoted context omitted.

Thanks for your explanation. For my region, 2024 coverage is already available, which should be sufficient to get started. After looking into the library, I just want to make sure I understand the workflow correctly: I would use the Tessera interactive map to mark known locations of Giant Hogweed, label them, and export as GeoJSON; then train a k-NN model, make predictions, and finally export the results as a GeoJSON…

So the interactive map should do this workflow for you. You place points and it will run the knn classifier over the landscape for you. If you want to go further you can export the GeoJSON and then run it through any machine learning pipeline you like.

...and if you do build this @ensocode, feel free to open a PR to https://github.com/ucam-eo/geotessera and I'll incorporate it as an example in the repo.

Re: Can a model trained on satellite data really find brambles on the ground?

#52

Post by avsm »

daemonologist wrote: post #28

The whole-earth embeddings are interesting. Wonder if it'd be any good for looking for fresh water sources in the desert.

Are you thinking of _new_ fresh water sources that emerge in recent years? If you have any candidate lat/lon where this might have happened, we can take a look at the 2024 and earlier embeddings to see if we can spot it.

Re: Can a model trained on satellite data really find brambles on the ground?

#53

Post by bluGill »

Waterluvian wrote: post #11
CrazyStat wrote: post #7

Earlier quoted context omitted.

> If you’re a good guy, you’d leverage this data to empower farmers. If you’re an asshole, you’re looking to see who has planted your crop illegally, or who is breaking your insurance fine print, etc. How does using it to speculate on crop futures rank?

Every time someone explains the way short selling or speculative markets work, I have a “oh, I get it…” moment and then forget months later. Same with insurance… socialized risk for our food supply is objectively good, and protecting the insurance mechanism from fraud is good. People can always bastardize these things.

It is complex. I was going to write out how it works in a simple way that everyone could understand - but then I realized that even though it would be a gross simplifications that are unrealistic, it still would be so complex that people would go "yep I understand that to every step", and then finish and not understand it. Every step alone makes perfects sense and is simple, but the total quickly gets complex.

Even calling this a speculative market is a gross simplification of the truth.

Re: Can a model trained on satellite data really find brambles on the ground?

#54

Post by bluGill »

wbl wrote: post #16
CrazyStat wrote: post #7

Earlier quoted context omitted.

> If you’re a good guy, you’d leverage this data to empower farmers. If you’re an asshole, you’re looking to see who has planted your crop illegally, or who is breaking your insurance fine print, etc. How does using it to speculate on crop futures rank?

It is good to enable people to hedge against bad harvests.

There are two sides hedging against bad harvests, the farmer that grows the crop, and the industry (cattle, ethanol, food oils, and others) that buys that crop. The farmer wants to get paid, and the industry wants to get their crop.
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